15.1k citations · 36.9k across the 152 of their papers we have counts for
21 papers · 1 filter
Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks
Andrey Kuzmin, Markus Nagel, Saurabh Pitre +3
The success of deep neural networks in many real-world applications is leading to new challenges in building more efficient architectures. One effective way of making networks more…
Learning Likelihoods with Conditional Normalizing Flows
Christina Winkler, Daniel Worrall, Emiel Hoogeboom +1
Normalizing Flows (NFs) are able to model complicated distributions p(y) with strong inter-dimensional correlations and high multimodality by transforming a simple base density p(z…
Invert to Learn to Invert
Patrick Putzky, Max Welling
Iterative learning to infer approaches have become popular solvers for inverse problems. However, their memory requirements during training grow linearly with model depth, limiting…
Contrastive Learning of Structured World Models
Thomas Kipf, Elise van der Pol, Max Welling
A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from ra…
i-RIM applied to the fastMRI challenge
Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen +4
We, team AImsterdam, summarize our submission to the fastMRI challenge (Zbontar et al., 2018). Our approach builds on recent advances in invertible learning to infer models as pres…
DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning
Frederik Harder, Jonas Köhler, Max Welling +1
Developing a differentially private deep learning algorithm is challenging, due to the difficulty in analyzing the sensitivity of objective functions that are typically used to tra…